Method for automatically judging data quality of double-view-field high-spectral-resolution laser radar

Abstract

The invention discloses a method for automatically judging the data quality of a double-view-field high-spectral-resolution laser radar. The method comprises the following steps: selecting characteristic parameters corresponding to mutant fault data; drawing probability density distribution of the corresponding characteristic parameters; determining a fuzzy membership function; establishing a mutability fault data discrimination equation and determining a corresponding weight and a threshold value; calculating an aerosol scattering ratio, and setting a threshold value; outputting a judgment result of the mutation fault data; selecting characteristic parameters corresponding to the regular fault data; establishing a sample set corresponding to the characteristic parameters; bootstrap sampling is carried out on the sample set; performing decision tree classification on sampling results; performing voting on the classification result; and outputting a judgment result of the regular fault data according to the voted mode. According to the invention, the method can achieve the automatic discrimination of the fault data of the high-spectral-resolution laser radar, reduces the labor cost, and also can effectively guarantee the reliability of a later data product.

Description

technical field [0001] The invention belongs to the technical field of atmospheric aerosol remote sensing lidar, and in particular relates to a method for automatically judging data quality of a dual-view field high-spectral resolution lidar. Background technique [0002] High-spectral-resolution lidar (High-Spectral-Resolution-Lidar, HSRL) is a new type of lidar system that can accurately invert aerosol and cloud optical characteristic parameters without assuming the lidar ratio, and is currently being used more and more widely. for the observation of aerosols. With the increasing importance of HSRL in the field of aerosol detection, the system has been successfully applied to the construction of lidar network. [0003] However, in the actual observation process, due to the influence of factors such as the working environment of the lidar and its own hardware quality, various problems may occur in the observation data. In addition, the laser radar system will generate a l...

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Problems solved by technology

[0006] However, the above-mentioned studies are only for discrimination or optimization of radar echo signal data, and have not achieved effective discrimination against problems caused by system hardware.

[0007] In addition, Zheng Chaoyang et al. proposed a discrimination method for ozone lidar hardware fault data, but it is mainly for discriminating abrupt fault data, and it is vulnerable to cloud interference when discriminating non-fault data

[0008] Generally speaking, as high-spectral resolution lidar is paid more and more attention today, there is still a lack of relevant research on the data quality discrimination method for this kind of system, and it is urgent to design a discrimination method for high-spectral resolution lidar data quality

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